431 lines
17 KiB
TypeScript
431 lines
17 KiB
TypeScript
import { OperateBookType, PromptMergeType } from '@/define/enum/bookEnum'
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import { Book } from '@/define/model/book/book'
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import { errorMessage, SendReturnMessage, successMessage } from '@/public/generalTools'
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import { cloneDeep, isEmpty } from 'lodash'
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import { AiReasonCommon } from '../../aiReason/aiReasonCommon'
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import { DEFINE_STRING } from '@/define/ipcDefineString'
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import { GeneralResponse } from '@/define/model/generalResponse'
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import { ExecuteConcurrently } from '@/define/Tools/common'
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import { OptionKeyName } from '@/define/enum/option'
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import { optionSerialization } from '../../option/optionSerialization'
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import { SettingModal } from '@/define/model/setting'
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import { MJServiceHandle } from '@/main/service/mj/mjServiceHandle'
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import { BookBasicHandle } from './bookBasicHandle'
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import { PresetCategory } from '@/define/data/presetData'
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import { ValidateJsonAndParse } from '@/define/Tools/validate'
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import { BookTask } from '@/define/model/book/bookTask'
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import { SDServiceHandle } from '../../sd/sdServiceHandle'
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import { aiHandle } from '../../ai'
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import { AICharacterAnalyseRequestData } from '@/define/data/aiData/aiPrompt/CharacterAndScene/aiCharacterAnalyseRequestData'
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import { AISceneAnalyseRequestData } from '@/define/data/aiData/aiPrompt/CharacterAndScene/aiSceneAnalyseRequestData'
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import { t } from '@/i18n'
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import { OptionRealmService } from '@/define/db/service/optionService'
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import { PresetModel } from '@/define/model/preset'
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import { AiInferenceModelModel } from '@/define/data/aiData/aiData'
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export class BookPromptHandle extends BookBasicHandle {
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aiReasonCommon: AiReasonCommon
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mjServiceHandle: MJServiceHandle
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sdServiceHandle: SDServiceHandle
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constructor() {
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super()
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this.aiReasonCommon = new AiReasonCommon()
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this.mjServiceHandle = new MJServiceHandle()
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this.sdServiceHandle = new SDServiceHandle()
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}
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/**
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* 为小说分镜生成AI提示词
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*
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* 该方法根据操作类型获取相应的分镜数据，然后使用AI推理为每个分镜生成提示词。
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* 支持三种操作模式：
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* 1. 对整个小说任务的所有分镜进行处理（BOOKTASK）
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* 2. 对单个指定分镜进行处理（BOOKTASKDETAIL）
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* 3. 对指定分镜及其后续所有分镜进行处理（UNDERBOOKTASK）
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*
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* 生成过程中会实时向前端发送进度通知，并根据系统设置控制并发处理数量。
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*
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* @param {string} id - 根据operateBookType不同，可能是小说任务ID或分镜ID
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* @param {OperateBookType} operateBookType - 操作类型，决定处理范围
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* @param {boolean} coverData - 是否覆盖已有提示词数据：
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* true-处理所有分镜, false-只处理空白提示词的分镜
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* @returns {Promise<SuccessItem | ErrorItem>} 操作结果，成功或失败的标准化响应
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*
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* @throws 如果找不到指定分镜数据或操作类型未知，将抛出异常
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*
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* @example
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* // 为整个小说任务生成提示词，不覆盖已有数据
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* const result = await bookPromptHandle.OriginalGetAiPrompt(
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* "task-123",
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* OperateBookType.BOOKTASK,
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* false
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* );
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*/
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OriginalGetAiPrompt = async (
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id: string,
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operateBookType: OperateBookType,
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coverData: boolean
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): Promise<GeneralResponse.ErrorItem | GeneralResponse.SuccessItem> => {
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try {
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let bookTask: Book.SelectBookTask = {} as Book.SelectBookTask
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let bookTaskDetails: Book.SelectBookTaskDetail[] = []
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let allBookTaskDetails: Book.SelectBookTaskDetail[] = []
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await this.InitBookBasicHandle()
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if (operateBookType == OperateBookType.BOOKTASK) {
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bookTask = await this.bookTaskService.GetBookTaskDataById(id, true)
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allBookTaskDetails = await this.bookTaskDetailService.GetBookTaskDetailDataByCondition({
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bookTaskId: id
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})
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if (!coverData) {
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// 不覆盖数据，只推理空白提示词
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bookTaskDetails = allBookTaskDetails.filter((item) => isEmpty(item.gptPrompt))
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} else {
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// 不覆盖，就是全部
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bookTaskDetails = allBookTaskDetails
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}
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} else if (operateBookType == OperateBookType.BOOKTASKDETAIL) {
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let singleBookTaskDetail = await this.bookTaskDetailService.GetBookTaskDetailDataById(id, true)
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bookTask = await this.bookTaskService.GetBookTaskDataById(
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singleBookTaskDetail.bookTaskId as string,
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true
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)
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bookTaskDetails = [singleBookTaskDetail]
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} else if (operateBookType == OperateBookType.UNDERBOOKTASK) {
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let singleBookTaskDetail = await this.bookTaskDetailService.GetBookTaskDetailDataById(id, true)
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bookTask = await this.bookTaskService.GetBookTaskDataById(
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singleBookTaskDetail.bookTaskId as string,
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true
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)
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// 获取全部的分镜数据
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allBookTaskDetails = await this.bookTaskDetailService.GetBookTaskDetailDataByCondition({
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bookTaskId: bookTask.id
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})
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// 只要当前行往下的数据
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for (let i = 0; i < allBookTaskDetails.length; i++) {
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const element = allBookTaskDetails[i]
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if (bookTaskDetails == undefined) {
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bookTaskDetails = []
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}
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if (i + 1 >= (singleBookTaskDetail.no as number)) {
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bookTaskDetails.push(element)
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}
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}
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} else {
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throw new Error(t('未知操作'))
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}
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if (!allBookTaskDetails || allBookTaskDetails.length <= 0) {
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allBookTaskDetails = await this.bookTaskDetailService.GetBookTaskDetailDataByCondition({
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bookTaskId: bookTask.id
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})
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}
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if (bookTaskDetails.length <= 0) {
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throw new Error(t('没有找到要推理的分镜数据'))
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}
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let generalSettingOption = this.optionRealmService.GetOptionByKey(
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OptionKeyName.Software.GeneralSetting
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)
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let generalSetting = optionSerialization<SettingModal.GeneralSettings>(
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generalSettingOption,
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t('设置 -> 通用设置')
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)
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let tasks = [] as Array<() => Promise<any>>
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// 获取当前task的角色和场景数据
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let autoAnalyzeCharacter = bookTask.autoAnalyzeCharacter ?? '{}'
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let autoAnalyzeCharacterData =
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ValidateJsonAndParse<BookTask.BookTaskCharacterAndScene>(autoAnalyzeCharacter)
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let characterData = autoAnalyzeCharacterData[PresetCategory.Character] ?? []
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let sceneData = autoAnalyzeCharacterData[PresetCategory.Scene] ?? []
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let characterString = ''
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let sceneString = ''
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if (characterData.length > 0) {
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characterString = characterData.map((item) => item.name + '：' + item.prompt).join('\n')
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characterString = '角色设定：' + '\n' + characterString
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}
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if (sceneData.length > 0) {
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sceneString = sceneData.map((item) => item.name + '：' + item.prompt).join('\n')
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sceneString = '场景设定：' + '\n' + sceneString
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}
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// 获取自定义的提示词数据
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let optionsData: AiInferenceModelModel[] = [];
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const optionRealmService = await OptionRealmService.getInstance();
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let customInferencePreset = optionRealmService.GetOptionByKey(OptionKeyName.InferenceAI.CustomInferencePreset);
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if (customInferencePreset != null && customInferencePreset.value != null && !isEmpty(customInferencePreset.value)) {
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let customInferencePresetData = optionSerialization<PresetModel.AIPresetTemplate[]>(customInferencePreset, t('设置 -> 推理设置 -> 自定义预设'), []);
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for (let i = 0; i < customInferencePresetData.length; i++) {
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const element = customInferencePresetData[i];
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optionsData.push({
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value: element.id,
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label: element.name,
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hasExample: element.hasExample,
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mustCharacter: element.mustCharacter,
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requestBody: element.requestBody,
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allAndExampleContent: null
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})
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}
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}
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// 添加异步任务
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for (let i = 0; i < bookTaskDetails.length; i++) {
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const element = bookTaskDetails[i]
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tasks.push(async () => {
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let content = await this.aiReasonCommon.OriginalInferencePrompt(
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element,
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allBookTaskDetails,
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15, // 上下文关联行数
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characterString,
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sceneString, optionsData
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)
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console.log(element.afterGpt, content)
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// 修改推理出来的数据
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await this.bookTaskDetailService.ModifyBookTaskDetailById(element.id as string, {
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gptPrompt: content
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})
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// 每次完成，都要向前端返回信息
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SendReturnMessage(
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{
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code: 1,
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id: element.id as string,
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data: {
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content: content,
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progress: {
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current: i,
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total: bookTaskDetails.length
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} as GeneralResponse.ProgressResponse
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}
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},
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DEFINE_STRING.BOOK.ORIGINAL_GET_AI_PROMPT_RETURN
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)
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})
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}
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// 分批次执行异步任务
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await ExecuteConcurrently(tasks, global.am.isPro ? (generalSetting.concurrency ?? 1) : 1)
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// 执行完毕
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return successMessage(null, t("推理所有数据完成"), 'BookPrompt_OriginalGetPrompt')
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} catch (error: any) {
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// 处理错误，返回错误信息
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return errorMessage(
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t("推理所有数据失败，{error}", {
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error: error.message
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}),
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'BookPromptHandle_OriginalGetAiPrompt'
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)
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}
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}
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/**
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* AI分镜头合并
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* @param bookTaskId 小说任务ID
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* @param type 合并类型
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* @returns 操作结果
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* @throws 操作失败时抛出异常
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* @example
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* const result = await bookPromptHandle.AIStoryboardMerge("task-123", BookTask.StoryboardMergeType.MJ);
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* if (result.code === 1) {
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* // 合并成功
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* } else {
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* // 合并失败
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* }
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*/
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AIStoryboardMerge = async (bookTaskId: string, type: BookTask.StoryboardMergeType) => {
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try {
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let res = await aiHandle.AIStoryboardMerge(bookTaskId, type)
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return successMessage(res, t('AI分镜头合并成功'), 'BookPromptHandle_AIStoryboardMerge')
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} catch (error: any) {
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return errorMessage(
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t('AI分镜头合并成功', {
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error: error.message
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}),
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'BookPromptHandle_AIStoryboardMerge'
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)
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}
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}
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/**
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* 合并提示词
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*
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* 该方法根据提供的合并类型(MJ或SD)，将分镜中的提示词按照规则进行合并处理。
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* 支持两种合并类型：
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* - MJ_MERGE: 使用MidJourney规则合并提示词
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* - SD_MERGE: 使用Stable Diffusion规则合并提示词
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*
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* 合并操作支持不同的处理范围，由operateBookType参数决定：
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* - BOOKTASK: 处理整个小说任务中的所有分镜
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* - BOOKTASKDETAIL: 仅处理单个指定分镜
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* - UNDERBOOKTASK: 处理指定分镜及其后续所有分镜
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*
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* @param {string} id - 根据operateBookType不同，可能是小说任务ID或分镜ID
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* @param {PromptMergeType} type - 合并类型，决定使用哪种规则合并提示词
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* @param {OperateBookType} operateBookType - 操作类型，决定处理范围
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* @returns {Promise<SuccessItem | ErrorItem>} 操作结果，成功或失败的标准化响应
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*
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* @throws {Error} 如果合并类型未知，将抛出异常
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*
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* @example
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* // 使用MidJourney规则合并单个分镜的提示词
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* const result = await bookPromptHandle.MergePrompt(
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* "detail-123",
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* PromptMergeType.MJ_MERGE,
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* OperateBookType.BOOKTASKDETAIL
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* );
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*/
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MergePrompt = async (
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id: string,
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type: PromptMergeType,
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operateBookType: OperateBookType
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): Promise<GeneralResponse.SuccessItem | GeneralResponse.ErrorItem> => {
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try {
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if (type == PromptMergeType.MJ_MERGE) {
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return await this.mjServiceHandle.MergeMJPrompt(id, operateBookType)
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} else if (type == PromptMergeType.SD_MERGE) {
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return await this.sdServiceHandle.MergeSDPrompt(id, operateBookType)
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} else {
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throw new Error(t('未知的合并模式，请检查'))
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}
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} catch (error: any) {
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return errorMessage(
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t("合并提示词失败，{error}", {
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error: error.message
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}),
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'ReverseBook_MergePrompt'
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)
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}
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}
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/**
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* 自动分析书籍任务中的角色或场景
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*
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* 该方法使用AI技术分析书籍任务中的所有分镜文本，自动识别并提取角色或场景信息。
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* 处理过程包括合并所有分镜文本、调用AI分析、解析返回结果并格式化为结构化数据。
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* 分析结果会更新到书籍任务的autoAnalyzeCharacter字段中，以JSON格式存储。
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*
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* @param {string} bookTaskId - 要分析的书籍任务ID
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* @param {PresetCategory} type - 分析类型，必须是PresetCategory.Character(角色)或PresetCategory.Scene(场景)
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*
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* @returns {Promise<GeneralResponse.SuccessItem | GeneralResponse.ErrorItem>}
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* 成功时返回分析得到的角色或场景数据对象数组，失败时返回错误信息
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*
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* @throws {Error} 当分析类型无效、找不到书籍任务数据或分镜数据为空时抛出错误
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*
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* @example
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* // 分析书籍任务中的角色
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* const characterResult = await bookPromptHandle.AutoAnalyzeCharacterOrScene(
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* "task-123",
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* PresetCategory.Character
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* );
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*
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* // 分析书籍任务中的场景
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* const sceneResult = await bookPromptHandle.AutoAnalyzeCharacterOrScene(
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* "task-123",
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* PresetCategory.Scene
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* );
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*/
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AutoAnalyzeCharacterOrScene = async (bookTaskId: string, type: PresetCategory): Promise<GeneralResponse.SuccessItem | GeneralResponse.ErrorItem> => {
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try {
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if (type != PresetCategory.Character && type != PresetCategory.Scene) {
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throw new Error(t('分析的类型只能是角色或场景，请检查'))
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}
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await this.InitBookBasicHandle()
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let bookTask = await this.bookTaskService.GetBookTaskDataById(bookTaskId, true)
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let bookTaskDetails = await this.bookTaskDetailService.GetBookTaskDetailDataByCondition({
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bookTaskId: bookTaskId
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})
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if (bookTaskDetails.length <= 0) {
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throw new Error(t('没有找到要分析的分镜数据，请先导入文案或者时srt！'))
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}
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let words = bookTaskDetails
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.map((item) => {
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return item.afterGpt
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})
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.join('\r\n')
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let requestData: OpenAIRequest.Request
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if (type == PresetCategory.Character) {
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requestData = cloneDeep(AICharacterAnalyseRequestData)
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} else if (type == PresetCategory.Scene) {
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requestData = cloneDeep(AISceneAnalyseRequestData)
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} else {
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throw new Error(t('未知的分析类型，请检查'))
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}
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requestData.messages = this.aiReasonCommon.replaceMessageObject(requestData.messages, {
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textContent: words
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})
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await this.aiReasonCommon.GetAISetting()
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delete requestData.model
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let content = await this.aiReasonCommon.FetchGpt(requestData.messages, requestData)
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let autoAnalyzeCharacter = bookTask.autoAnalyzeCharacter ?? '{}'
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let autoAnalyzeCharacterData =
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ValidateJsonAndParse<BookTask.BookTaskCharacterAndScene>(autoAnalyzeCharacter)
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let returnData = content.split('\n').filter((item) => !isEmpty(item))
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let newData: BookTask.BookTaskCharacterAndSceneObject[] = []
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for (let i = 0; i < returnData.length; i++) {
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const element = returnData[i]
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if (type == PresetCategory.Character) {
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let splitData = element.split('：')
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if (splitData.length < 2) {
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continue
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}
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let tempData = {
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no: i + 1,
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id: crypto.randomUUID(),
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name: splitData[0],
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prompt: splitData[1]
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} as BookTask.BookTaskCharacterAndSceneObject
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newData.push(tempData)
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} else if (type == PresetCategory.Scene) {
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let splitData = element.split('.')
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if (splitData.length < 3) {
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continue
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}
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let tempData = {
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no: Number(splitData[0]),
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id: crypto.randomUUID(),
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name: splitData[1],
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prompt: splitData[2]
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} as BookTask.BookTaskCharacterAndSceneObject
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newData.push(tempData)
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}
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}
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if (type == PresetCategory.Character) {
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autoAnalyzeCharacterData[PresetCategory.Character] = newData
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} else if (type == PresetCategory.Scene) {
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// 场景数据
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autoAnalyzeCharacterData[PresetCategory.Scene] = newData
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}
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// 重新写入
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await this.bookTaskService.ModifyBookTaskDataById(bookTaskId, {
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autoAnalyzeCharacter: JSON.stringify(autoAnalyzeCharacterData)
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})
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return successMessage(
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autoAnalyzeCharacterData,
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t('自动分析角色或场景成功'),
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'ReverseBook_AutoAnalyzeCharacterOrScene'
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)
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} catch (error: any) {
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return errorMessage(
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t("自动分析角色或场景失败，{error}", {
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error: error.message
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}),
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'ReverseBook_AutoAnalyzeCharacterOrScene'
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)
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}
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}
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}
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